scholarly journals Computational Thinking for All: Pedagogical Approaches to Embedding 21st Century Problem Solving in K-12 Classrooms

TechTrends ◽  
2016 ◽  
Vol 60 (6) ◽  
pp. 565-568 ◽  
Author(s):  
Aman Yadav ◽  
Hai Hong ◽  
Chris Stephenson
Keyword(s):  
Problem Solving ◽  
21St Century ◽  
Computational Thinking ◽  
K 12 ◽  
Pedagogical Approaches

scholarly journals A Revaluation of Computational Thinking in K–12 Education: Moving Toward Computational Literacies

2021 ◽  
pp. 0013189X2110579
Author(s):  
Yasmin B. Kafai ◽  
Chris Proctor
Keyword(s):  
21St Century ◽  
Computational Thinking ◽  
Creative Expression ◽  
Career Readiness ◽  
College And Career Readiness ◽  
Computing Education ◽  
The Past ◽  
College And Career ◽  
The World ◽  
K 12

Over the past decade, initiatives around the world have introduced computing into K–12 education under the umbrella of computational thinking. While initial implementations focused on skills and knowledge for college and career readiness, more recent framings include situated computational thinking (identity, participation, creative expression) and critical computational thinking (political and ethical impacts of computing, justice). This expansion reflects a revaluation of what it means for learners to be computationally-literate in the 21st century. We review the current landscape of K–12 computing education, discuss interactions between different framings of computational thinking, and consider how an encompassing framework of computational literacies clarifies the importance of computing for broader K–12 educational priorities as well as key unresolved issues.

Analyzing Current Visual Tools and Methodologies of Computer Programming Teaching in Primary Education

2018 ◽  
pp. 201-229
Author(s):  
Serhat Altiok ◽  
Erman Yükseltürk
Keyword(s):  
Creative Thinking ◽  
Computer Programming ◽  
Collaborative Work ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Visual Programming ◽  
General Description ◽  
Programming Tools ◽  
K 12 ◽  
Pedagogical Approaches

In our age, computational thinking that involves understanding human behavior and designing systems for solving problems is important as much as reading, writing and arithmetic for everyone. Computer programming is one of the ways that could be promote the process of developing computational thinking, in addition to developing higher-order thinking skills such as problem solving, critical and creative thinking skills etc. However, instead of focusing on problems and sub-problems, algorithms, or the most effective and efficient solution, focusing on programming language specific needs and problems affects the computational thinking process negatively. Many educators use different tools and pedagogical approaches to overcome these difficulties such as, individual work, collaborative work and visual programming tools etc. In this study, researchers analyze four visual programming tools (Scratch, Small Basic, Alice, App Inventor) for students in K-12 level and three methodologies (Project-based learning, Problem-based learning and Design-based learning) while teaching programming in K-12 level. In summary, this chapter presents general description of visual programming tools and pedagogical approaches, examples of how each tool can be used in programming education in accordance with the CT process and the probable benefits of these tools and approaches to explore the practices of computational thinking.

Analyzing Current Visual Tools and Methodologies of Computer Programming Teaching in Primary Education

2022 ◽  
pp. 648-676
Author(s):  
Serhat Altiok ◽  
Erman Yükseltürk
Keyword(s):  
Creative Thinking ◽  
Computer Programming ◽  
Collaborative Work ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Visual Programming ◽  
General Description ◽  
Programming Tools ◽  
K 12 ◽  
Pedagogical Approaches

In our age, computational thinking that involves understanding human behavior and designing systems for solving problems is important as much as reading, writing and arithmetic for everyone. Computer programming is one of the ways that could be promote the process of developing computational thinking, in addition to developing higher-order thinking skills such as problem solving, critical and creative thinking skills etc. However, instead of focusing on problems and sub-problems, algorithms, or the most effective and efficient solution, focusing on programming language specific needs and problems affects the computational thinking process negatively. Many educators use different tools and pedagogical approaches to overcome these difficulties such as, individual work, collaborative work and visual programming tools etc. In this study, researchers analyze four visual programming tools (Scratch, Small Basic, Alice, App Inventor) for students in K-12 level and three methodologies (Project-based learning, Problem-based learning and Design-based learning) while teaching programming in K-12 level. In summary, this chapter presents general description of visual programming tools and pedagogical approaches, examples of how each tool can be used in programming education in accordance with the CT process and the probable benefits of these tools and approaches to explore the practices of computational thinking.

scholarly journals Defining a New 21st Century Skill-Computational Thinking: Concepts and Trends

2018 ◽  
Vol 11 (4) ◽  
pp. 29 ◽  
Author(s):  
Halil Ibrahim Haseski ◽  
Ulas Ilic ◽  
Ufuk Tugtekin
Keyword(s):  
Content Analysis ◽  
Problem Solving ◽  
21St Century ◽  
Daily Life ◽  
Computational Thinking ◽  
Comprehensive Approach ◽  
21St Century Skill

Computational Thinking is a skill that guides the 21th century individual in the problems experienced during daily life and it has an ever-increasing significance. Multifarious definitions were attempted to explain the concept of Computational Thinking. However, it was determined that there was no consensus on this matter in the literature and several different concepts were mentioned in the definitions found in the literature. It was considered that this fact made it difficult to understand the concept of Computational Thinking. To establish a more comprehensive approach, the present study aimed to identify the concepts that are included in the Computational Thinking definitions that were presented in previous studies. It also aimed to reveal trends in the identified concepts throughout the years. As a result of the search, a total of 59 definitions were identified and a content analysis was conducted on these definitions. Analysis results demonstrated that Computational Thinking was defined based on several concepts such as problem solving, technology, thinking, individual and social qualities. Furthermore, it was determined that statements on thinking were prominent before 2006, and today, emphasis on problem solving and technology became more significant. It was considered that the present study would contribute to a better understanding of the Computational Thinking concept. At the end of the study, certain suggestions were presented for further research.

scholarly journals Examining Student Regulation of Collaborative, Computational, Problem-Solving Processes in Open-Ended Learning Environments

2021 ◽  
Vol 8 (1) ◽  
pp. 49-74
Author(s):  
Mona Emara ◽  
Nicole Hutchins ◽  
Shuchi Grover ◽  
Caitlin Snyder ◽  
Gautam Biswas
Keyword(s):  
Problem Solving ◽  
Language Processing ◽  
Computational Models ◽  
Model Building ◽  
Computational Modelling ◽  
Computational Thinking ◽  
Collaborative Problem Solving ◽  
Model Errors ◽  
Systematic Analysis ◽  
K 12

The integration of computational modelling in science classrooms provides a unique opportunity to promote key 21st century skills including computational thinking (CT) and collaboration. The open-ended, problem-solving nature of the task requires groups to grapple with the combination of two domains (science and computing) as they collaboratively construct computational models. While this approach has produced significant learning gains for students in both science and CT in K–12 settings, the collaborative learning processes students use, including learner regulation, are not well understood. In this paper, we present a systematic analysis framework that combines natural language processing (NLP) of collaborative dialogue, log file analyses of students’ model-building actions, and final model scores. This analysis is used to better understand students’ regulation of collaborative problem solving (CPS) processes over a series of computational modelling tasks of varying complexity. The results suggest that the computational modelling challenges afford opportunities for students to a) explore resource-intensive processes, such as trial and error, to more systematic processes, such as debugging model errors by leveraging data tools, and b) learn from each other using socially shared regulation (SSR) and productive collaboration. The use of such SSR processes correlated positively with their model-building scores. Our paper aims to advance our understanding of collaborative, computational modelling in K–12 science to better inform classroom applications.

Makerspaces as Learning Environments to Support Computational Thinking

2021 ◽  
pp. 176-200
Author(s):  
Amanda L. Strawhacker ◽  
Miki Z. Vizner
Keyword(s):  
Problem Solving ◽  
Learning Outcomes ◽  
Learning Environments ◽  
21St Century ◽  
Cognitive Processes ◽  
Physical Environment ◽  
Computational Thinking ◽  
21St Century Learning ◽  
Specific Task ◽  
Technological Tools

Makerspaces are technology-rich learning environments that can uniquely support children's development. In education communities, makerspaces have become sites to take up explorations of personally-motived problem solving, and have been tied to 21st century learning outcomes of perseverance, creativity, persistence, and computational thinking. Elsewhere in this book, Bers described computational thinking as the set of skills and cognitive processes required to give instructions for a specific task in such a way that a computer could carry it out. But Bers also argued that the purpose of computational thinking is to cultivate a fluency with technological tools as a medium of expression, not an end in itself. Computational making is part of this expression. This chapter explores the ways in which tools, facilitation, and the physical environment can support children's engagement with powerful ideas of computational thinking through making.

scholarly journals Kontribusi Seesaw sebagai assesment for learning dalam mengukur keterampilan berpikir abad 21

2020 ◽  
Vol 3 (2) ◽  
pp. 51-54
Author(s):  
Eva Fadilah ◽  
Ana Ratna Wulan ◽  
Sariwulan Diana
Keyword(s):  
Problem Solving ◽  
21St Century ◽  
Thinking Skills ◽  
Project Based Learning ◽  
National Assessment ◽  
Problem Solving Skills ◽  
Gain Scores ◽  
K 12 ◽  
Learning Projects ◽  
Education Standard

The 21st Century thinking skills are important skills to have for everyone. These skills can be developed through learning and assessment with the help of applications. This study aims to measure 21st century problem solving skills using Seesaw as an assessment for learning. The method used is descriptive. This research was conducted in one class 10 of SMAN X in West Bandung which was selected by purposive sampling. This study used three project based learning projects. The instruments used were tasks, test instruments, and questionnaires. The task is uploaded in the Seesaw application and assessed with a rubric which is modified from the K-12 Public Education standard 2015. The test instrument is adapted from the NAEP (National Assessment of Educational Progress). The results showed that there was an increase in students' problem solving skills with more N-gain scores in the high category. The mastery of problem solving skills was more in mastery. The progress of students' problem-solving skills also increased during the completion of task 1 to task 3.

scholarly journals Effects of Robotics Instructional Methods on Computational Thinking Skills of Middle School Students

2021 ◽  
Author(s):  
Andrew Patrick Cook
Keyword(s):  
Middle School ◽  
Problem Solving ◽  
Middle School Students ◽  
Instructional Methods ◽  
Computational Thinking ◽  
Thinking Skills ◽  
Peer Collaboration ◽  
School Students ◽  
Problem Solving Strategies ◽  
K 12

As a tangible and motivating medium for students to engage in computational thinking, robotics has drawn interest from educators and researchers as K-12 schools continue to integrate STEM into curriculum. Through this mixed methods study, the researcher sought to explore the effects of robotics instructional methods (task-based and project-based) on the computational thinking skills of middle school students, including the problem-solving strategies used and the role of peer collaboration. The quantitative results of this study indicated no significant difference in the computational thinking skills of students participating in task-based or project-based robotics instruction. Interviews consisted of open-ended questions in which problem-solving and collaboration in robotics were explored from the perspectives of the participants. In both groups, problem-solving strategies encompassed all aspects of computational thinking as students took an iterative approach to problem-solving in both tasks and projects. Peer collaboration was naturally occurring and frequent among both groups. In task-based robotics instruction, peer collaboration and problem-solving strategies were primarily focused on the programming of the robot. In project-based robotics, peer collaboration and problem-solving strategies were applied throughout the entire design process, including the building and the programming of the robot. Through this study, the researcher hoped to provide a roadmap for the implementation of robotics in schools for K-8 students. As schools are increasingly seeking ways to integrate robotics into school curriculum, further research in this area on a larger scale is recommended.

Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools

2022 ◽  
pp. 46-59
Author(s):  
Scott R. Garrigan
Keyword(s):  
Professional Development ◽  
Machine Learning ◽  
Problem Solving ◽  
Computational Thinking ◽  
Original Description ◽  
Development Framework ◽  
Child Centered ◽  
Creative Problem ◽  
K 12 ◽  
Children’S Thinking

Computational thinking (CT) K-12 curricula and professional development should prepare students for their future, but historically, such curricula have limited success. This chapter offers historical analogies and ways that CT curricula may have a stronger and more lasting impact. Two frameworks are central to the chapter's arguments. The first recalls Seymour Papert's original description of CT as a pedagogy with computing playing a formative role in young children's thinking; the computer was a tool to think with (1980, 1996). This “thinking development” framework emphasized child-centered, creative problem solving to foster deep engagement and understanding. Current CT seems to include creativity only tangentially. The second framework encompasses emergent machine learning and data concepts that will become pervasive. This chapter, more prescriptive than empirical, suggests ways that CT and requisite professional development could be more future-focused and more successful. It could be titled “Seymour Papert meets Machine Learning.”

Frameworks for Integration of Future-Oriented Computational Thinking in K-12 Schools

2020 ◽  
pp. 30-44
Author(s):  
Scott R. Garrigan
Keyword(s):  
Professional Development ◽  
Machine Learning ◽  
Problem Solving ◽  
Computational Thinking ◽  
Original Description ◽  
Development Framework ◽  
Child Centered ◽  
Creative Problem ◽  
K 12 ◽  
Children’S Thinking

Computational thinking (CT) K-12 curricula and professional development should prepare students for their future, but historically, such curricula have limited success. This chapter offers historical analogies and ways that CT curricula may have a stronger and more lasting impact. Two frameworks are central to the chapter's arguments. The first recalls Seymour Papert's original description of CT as a pedagogy with computing playing a formative role in young children's thinking; the computer was a tool to think with (1980, 1996). This “thinking development” framework emphasized child-centered, creative problem solving to foster deep engagement and understanding. Current CT seems to include creativity only tangentially. The second framework encompasses emergent machine learning and data concepts that will become pervasive. This chapter, more prescriptive than empirical, suggests ways that CT and requisite professional development could be more future-focused and more successful. It could be titled “Seymour Papert meets Machine Learning.”

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